The Future of Clinical Research
Intelligence that accelerates clinical trials
Find the right patients at the right sites — before your trial begins
From self-reported guesses to data-derived certainty. Axiom AI replaces site questionnaires with independent patient signal, biomarker-verified counts, and predictive enrollment modeling — a new era of protocol intelligence.
The Problem
Sponsors commit to sites before knowing if eligible patients truly exist
80%
of trials fail to meet initial enrollment targets and timelines
$540K
burned per day of delay — $500K in patent window, plus $40K in direct daily trial costs
50%
of activated sites enroll one patient or zero — each wasting $15K–$30K in initiation costs
8 wk
average CRO feasibility process — based entirely on site self-reporting
Biomarker Blind Spot
Site questionnaires face an additional failure: sites cannot verify whether their patients carry the specific biomarkers your protocol requires. For example, in oncology trials, a site may treat 300 NSCLC patients per year but cannot tell you how many are EGFR-mutant, HER2-positive, or MSI-H — the exact eligibility criteria that define modern targeted therapy trials. No questionnaire captures this. Only lab claims and genomic data do.
How Protocol Intelligence Works
Three-layer architecture
Protocol Ingestion
Any protocol to structured queries in minutes
AI parses every inclusion and exclusion criterion from your protocol document into structured medical code queries — ICD-10, CPT, NDC automatically. No manual translation, no analyst hours. Any protocol format accepted and converted in minutes.
Protocol parser
Any protocol → structured code queries in minutes
Patient Signal Mapping
Biomarker-verified patient counts by geography
Protocol criteria are run against 330M+ de-identified US patient records — refreshed weekly, not quarterly or annually like most RWD sources. Critically, the dataset includes lab claims and genomic data, so biomarker criteria (EGFR, HER2, BRCA, MSI-H) can be mapped against real patient records — something no site questionnaire can produce.
Patient map query
Independent patient prevalence — not site estimates
Enrollment Prediction
Confidence-scored site rankings in 48 hours
A predictive model trained on 400,000+ historical trials from the AACT database scores each geography and site type on probability of meeting your enrollment target within planned timeline — with optimistic, base, and pessimistic scenarios.
Enrollment predictor
Probability scores per site and geography, with confidence intervals
What Changes for Sponsors
Data-driven feasibility changes everything
Pre-IND
Protocol failure caught before lock
Know which I/E criteria eliminate 40%+ of your eligible population before the protocol is final — when a change costs weeks, not months and millions.
50% fewer
Underperforming sites activated
Only activate sites where patient density and biomarker prevalence support your target. At $15K–$30K per site initiation, eliminating zero-enrollment sites before contracts are signed is immediate, recoverable budget.
8 wks → 48 hrs
Feasibility in days, not months
Replace the questionnaire cycle with a data query. Site selection decisions that took two months now take two days — before a single contract is negotiated.
$540K/day
Cost of every day of delay
Each delayed day consumes $500K of patent window that can never be recovered post-approval — plus $40K in direct daily trial costs that are real spend right now.
See your feasibility report in 48 hours
Send us your protocol. We'll return a Feasibility Intelligence Report with biomarker-verified patient counts, site rankings, protocol risk flags, and enrollment timeline projections — in 48–72 hours.
Request Your ReportGet in Touch
Ready to see your protocol's feasibility signal?
Tell us about your trial and we'll show you what data-driven feasibility looks like for your protocol.